Datasets:

bunyaminergen
/

Cornstack-Python-V1-Filtered

query stringlengths 33 521	document stringlengths 8 49.6k	metadata dict	negatives sequencelengths 5 101	negative_scores sequencelengths 5 101	document_score stringlengths 3 10	document_rank stringclasses 102 values
Compute the matrixvector product y = Cu where C is a circulant matrix All matrices are real	def circulant_multiplication(u, a): return real(ifft(fft(a)*fft(u)))	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	[ "def covar(fx,cx):\n \n fx = np.array(fx)\n cx = np.array(cx)\n \n shape_fx = fx.shape\n shape_cx = cx.shape\n \n \n if shape_fx[1] != shape_cx[0]:\n print('-----------------------------------------')\n print(\"Shapes of fx and cx cannot be multiplied:\")\n print(shap...	[ "0.650418", "0.650212", "0.6441079", "0.6313763", "0.6310517", "0.62949276", "0.62782884", "0.62631303", "0.61975265", "0.6096459", "0.608041", "0.606508", "0.6038961", "0.6011421", "0.60068315", "0.59920776", "0.59303707", "0.58836865", "0.5879482", "0.58772385", "0.58575416...	0.6389226	3
Compute the matrixvector product y = Tu where T is a Toeplitz matrix All matrices are real	def toeplitz_multiplication(u, c, r=None): n = len(u) if r is None: r = c u1 = zeros((2*n)) u1[0:n] = u c = np.concatenate((c, [0], r[-1:0:-1])) y1 = circulant_multiplication(u1, c) return y1[0:n]	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	[ "def matrix_vector_prod(m,u):\n each_product = []\n for v in m:\n each_product.append(dot_prod(v, u))\n return each_product", "def matmul(x, y):\n if len(list(y.size())) == 2:\n # if one of them is a vector (i.e. wanting to do MV mult)\n z = torch.zeros(2, x.size()[1], dtype=torch...	[ "0.7003199", "0.6513981", "0.64759356", "0.6454179", "0.6377554", "0.6326698", "0.6245358", "0.620894", "0.6208685", "0.61977005", "0.6195611", "0.61694974", "0.6168602", "0.6134469", "0.6106113", "0.60868716", "0.6082444", "0.60823506", "0.6070701", "0.60688484", "0.6063607"...	0.63380134	5
Read in labels from digitStruct.mat file to create a dict of image file name and corresponding labels	def read_labels(digitstruct_file): labels = dict() for dsObj in tdqm(yieldNextDigitStruct(digitstruct_file), ncols=50): image_labels = [] for bbox in dsObj.bboxList: image_labels.append(bbox.label) labels[dsObj.name] = image_labels return labels	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	[ "def _get_imagenet_as_dict(self):\n real_file_path = os.path.realpath(self.map_file)\n if not os.path.exists(real_file_path):\n raise IOError(\"map file {} not exists\".format(self.map_file))\n\n label_dict = {}\n with open(real_file_path) as fp:\n line = fp.readlin...	[ "0.7442581", "0.67145514", "0.6680717", "0.66700083", "0.6651974", "0.6599294", "0.65706545", "0.6568262", "0.65624034", "0.65466106", "0.6527709", "0.65229243", "0.65100825", "0.6500305", "0.649048", "0.6466592", "0.6466018", "0.6442053", "0.6429563", "0.6409631", "0.6398935...	0.8415674	0
"ref CLRS pg326, solution to the basic supply chain problem using the book notation for variables na(...TRUNCATED)	"def fastestWay( a, t, e, x, n ):\n import pdb;pdb.set_trace() \n f1.append( ( e[0] , 1 ) )\n (...TRUNCATED)	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	["def exercise_b2_39():\r\n pass","def exercise_b2_113():\r\n pass","def exercise_b2_93():\r\n(...TRUNCATED)	["0.5789567","0.5612758","0.56002617","0.5582453","0.5527549","0.5454671","0.5450963","0.5440792","0(...TRUNCATED)	0.0	-1
This function computes the fundamental matrix by computing the SVD of Ax = 0 ; 8point algorithm	"def computeFundamentalMatrix(pts1, pts2):\n A = np.empty((8, 9))\n for i in range(len(pts1)-1(...TRUNCATED)	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	["def svd0(A):\n M,N = A.shape\n if M>N: return sla.svd(A, full_matrices=True)\n else: return s(...TRUNCATED)	["0.66048837","0.6466162","0.6259937","0.6250825","0.62505597","0.62274474","0.6104567","0.6089218",(...TRUNCATED)	0.68444854	0
"Leverages the 8point algorithm and implement RANSAC algorithm to find the inliers and the best fund(...TRUNCATED)	"def getInlierRANSAC(pts1, pts2):\n # global finalFundamentalMatrix\n iterations = 50\n thr(...TRUNCATED)	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	["def ransac(data, hypothesis, metric, sample_size, num_iter, inlier_thresh):\n N,d = data.shape\(...TRUNCATED)	["0.6203597","0.5916464","0.5894118","0.5867515","0.5715989","0.56956524","0.56905115","0.5686345","(...TRUNCATED)	0.699575	0
"=========================================================== DateFormatedSQL(x) ====================(...TRUNCATED)	"def DateFormatedSQL(x):\n x=[i[0] for i in x]\n \n x1=[]\n for i in x:\n if len((...TRUNCATED)	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	["def change_format_from_input_to_datetime(list_d_t_t):\n data_output = []\n\n for row in list(...TRUNCATED)	["0.66734904","0.65000284","0.6259414","0.59757656","0.5600508","0.5579302","0.5578522","0.5551475",(...TRUNCATED)	0.7940835	0
"=========================================================== dateformated(x) =======================(...TRUNCATED)	"def DateFormated(x):\n \n x1=[]\n for i in x:\n if len(i)==19:\n x1.appe(...TRUNCATED)	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	["def change_format_from_input_to_datetime(list_d_t_t):\n data_output = []\n\n for row in list(...TRUNCATED)	["0.73220545","0.6644235","0.64673054","0.63785565","0.6323779","0.63159305","0.6256937","0.6174311"(...TRUNCATED)	0.75249213	0
Mimic the & operator in R. This has to have Expression objects to be involved to work	"def _op_and_(self, left: Any, right: Any) -> Any:\n if isinstance(left, list):\n (...TRUNCATED)	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	["def AND(f, g):\n def _and(x):\n return f(x) & g(x)\n return _and","def and_(a, b):","(...TRUNCATED)	["0.6913351","0.6844835","0.6834847","0.68041515","0.6614185","0.6585983","0.65602845","0.65299505",(...TRUNCATED)	0.62697506	20
Mimic the & operator in R. This has to have Expression objects to be involved to work	"def _op_or_(self, left: Any, right: Any) -> Any:\n if isinstance(left, list):\n r(...TRUNCATED)	{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }	["def AND(f, g):\n def _and(x):\n return f(x) & g(x)\n return _and","def and_(a, b):","(...TRUNCATED)	["0.6913351","0.6844835","0.6834847","0.68041515","0.6614185","0.6585983","0.65602845","0.65299505",(...TRUNCATED)	0.0	-1

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YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Cornstack Python v1 Filtered

The Cornstack Python v1 Filtered dataset is derived from the nomic-ai/cornstack-python-v1 dataset by limiting queries to a maximum of 17 words and restricting the total number of rows to 423259. This dataset is suitable for Python programming education and question-answering applications.

Note: If you would like to contribute to this repository, please read the CONTRIBUTING first.

Features

Name: Cornstack Python v1 Filtered
Primary Purpose: Contains query-document pairs with corresponding Python code implementations, focusing primarily on matrix and vector operations (e.g., matrix-vector multiplication, circulant and Toeplitz matrices), along with associated negative samples for machine learning tasks like code retrieval and similarity modeling.
Language: English
Format: JSONL
License: GPL-3.0

File Structure

.
├── CONTRIBUTING.md
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── shard-00.jsonl.gz
├── shard-01.jsonl.gz
├── shard-02.jsonl.gz
├── shard-03.jsonl.gz
└── shard-04.jsonl.gz

1 directory, 10 files

Metadata

Data Dictionary

The dataset contains pairs of queries and documents with associated metadata, negative examples, and scoring information.

CSV

Column	Description	Type
`query`	Textual query or instruction	string
`document`	Relevant code snippet or textual response	string
`negatives`	List of non-relevant code snippets	list[string]
`metadata`	JSON object containing additional structured information	JSON object
`negative_scores`	List of scores corresponding to each negative example	list[float]
`document_score`	Score for the document	float
`document_rank`	Rank or category label for the document	string

Example row (CSV):

query	document	negatives	negative_scores	document_score	document_rank	metadata
Compute the matrixvector product y = Cu where C is a circulant matrix All matrices are real	def circulant_multiplication(u, a): return real(ifft(fft(a)*fft(u)))	['def covar(fx,cx): ...', 'def matmul(self, q: np.ndarray): ...']	[0.7675772, 0.6984068]	0.69579995	2	{"objective": {"self": [], "paired": [], "triplet": [["query", "document", "negatives"]]}}

JSON Lines

Each line represents one JSON object with the following structure:

{
  "query": "string, textual query or instruction",
  "document": "string, relevant code snippet or textual response",
  "negatives": ["list of strings, non-relevant code snippets"],
  "negative_scores": ["list of floats, scores for each negative example"],
  "document_score": "float, score for the document",
  "document_rank": "string, rank or category label",
  "metadata": {
      "objective": {
          "self": "list, self-related metadata (often empty)",
          "paired": "list, pairwise metadata (often empty)",
          "triplet": [["query", "document", "negatives"]]
      }
  }
}

Example row (JSONL):

{
  "query":"Compute the matrixvector product y = Cu where C is a circulant matrix All matrices are real",
  "document":"def circulant_multiplication(u, a): return real(ifft(fft(a)*fft(u)))",
  "negatives":[
    "def covar(fx,cx): ...",
    "def __matmul__(self, q: np.ndarray): ..."
  ],
  "negative_scores":[
    0.7675772,
    0.6984068
  ],
  "document_score":0.69579995,
  "document_rank":"2",
  "metadata":{
    "objective":{
      "self":[ ],
      "paired":[ ],
      "triplet":[
        [
          "query",
          "document",
          "negatives"
        ]
      ]
    }
  }
}

Usage

Hugging Face

from datasets import load_dataset

# 141k:
dataset_141k = load_dataset("bunyaminergen/cornstack-python-v1-filtered", revision="v3", split="train")
print(dataset_141k[0])

# 282k:
dataset_282k = load_dataset("bunyaminergen/cornstack-python-v1-filtered", revision="v5", split="train")
print(dataset_282k[0])

# 423k:
dataset_423k = load_dataset("bunyaminergen/cornstack-python-v1-filtered", revision="v7", split="train")
print(dataset_423k[0])

Versioning

v3: 141k version
v5: 282k version
v7: 423k version

Reference

This dataset is derived from the original dataset nomic-ai/cornstack-python-v1.

Citation

@misc{           CornstackPythonv1Filtered,
  author       = {Bunyamin Ergen},
  title        = {CornstackPythonv1Filtered},
  year         = {2025},
  month        = {03},
  url          = {https://huggingface.co/datasets/bunyaminergen/cornstack-python-v1-filtered},
}